NILG.AI
Editor's pick #1Porto-based AI consultancy founded by a University of Porto PhD, known for AI education as much as consulting delivery.
Founded 2018 | Porto, Portugal | 10–49 employees | Last updated: July 2026
ai-consultingml-developmentdata-engineering
What is NILG.AI?
NILG.AI is a Porto, Portugal AI consultancy founded in 2018 by Kelwin Fernandes (PhD, Computer Science, University of Porto) and Nohelia González. It runs a structured discover-pilot-scale methodology to help businesses identify high-impact AI opportunities, validate them, and scale what works, and has assisted over 100 companies across sectors. The company was incubated at UPTEC and was awarded Data Changemaker of the Year at DSPA Insights 2024 for an AI-driven urban waste-management project in the Algarve. Its YouTube education channel has over 100,000 subscribers and NILG.AI was selected for Microsoft's 'Learn with Creators' program.
NILG.AI was founded in 2018 and is headquartered in Porto, Portugal. The firm employs 10–49 people and works primarily with clients in Public Sector, Cross-industry AI adoption sectors. Its primary differentiator is: Founder-led by a University of Porto PhD with a public AI-education arm (100K+ YouTube subscribers, Microsoft education partner) that doubles as a technical credibility signal..
NILG.AI tech stack and services
Pythonscikit-learnData pipelinesCloud ML platforms
| Service area | Details |
|---|---|
| AI opportunity discovery workshops | Available for Public Sector, Cross-industry AI adoption clients |
| Municipal and public-sector optimization pilots | Available for Public Sector, Cross-industry AI adoption clients |
| Small-team AI upskilling and training | Available for Public Sector, Cross-industry AI adoption clients |
| Early-stage AI pilot-to-production scaling | Available for Public Sector, Cross-industry AI adoption clients |
NILG.AI use cases
Short answer: NILG.AI is best suited for companies earlier in their AI adoption curve that want a structured discover-pilot-scale engagement model rather than a from-scratch build..
| Use case | Industries | Approach |
|---|---|---|
| AI opportunity discovery workshops | Public Sector, Cross-industry AI adoption | Python, scikit-learn |
| Municipal and public-sector optimization pilots | Public Sector, Cross-industry AI adoption | Python, scikit-learn |
| Small-team AI upskilling and training | Public Sector, Cross-industry AI adoption | Python, scikit-learn |
| Early-stage AI pilot-to-production scaling | Public Sector, Cross-industry AI adoption | Python, scikit-learn |
NILG.AI pricing
Short answer: NILG.AI uses a consulting engagement, pilot-to-scale retainer pricing approach. Minimum engagement starts at Not published.
| Engagement model | Typical range | Best for |
|---|---|---|
| Consulting retainer | Monthly rate; not public | Ongoing AI engineering |
| Fixed-scope pilot | From Not published | Well-defined scope |
NILG.AI does not publish a public rate card. Contact them directly via their website to get project-specific pricing.
NILG.AI pros and cons
| Advantages | Things to consider |
|---|---|
| +Founder-level technical credibility (PhD-led, Microsoft education partner) uncommon at this company size | -10–49 employee band limits capacity for running several large programs concurrently |
| +Structured discovery-pilot-scale methodology reduces risk for first-time AI buyers | -Heavier emphasis on strategy and pilot work than large-scale production ML engineering compared to bigger players |
| +Public recognition (Data Changemaker of the Year 2024) for a real municipal deployment | -Public case studies skew toward public-sector and education rather than regulated enterprise sectors |
| +Incubated at UPTEC, giving it ties into Porto's applied-research ecosystem |
NILG.AI vs alternatives
How NILG.AI compares to the other top Machine Learning Development companies.
| Company | Best for | Key difference | Rating | Compare |
|---|---|---|---|---|
| dida Datenschmiede | Organizations that need a tightly-scoped, research-grade ML solution... | Team composed primarily of mathematicians and physicists, explicitly rejecting black-box tooling in favor of custom-built models as its sole service line. | 4.8 | Full comparison |
| Tensorway | Mid-market fintech, energy, and supply-chain companies that want... | Spun out of Anadea's applied R&D unit in 2019, giving it a mature delivery bench uncommon for a five-year-old AI boutique. | 4.6 | Full comparison |
| Neurons Lab | Financial-services firms that need agentic AI systems with... | Positions itself as an end-to-end AI enablement partner specifically for financial services, with governance and compliance tooling built into the core offer rather than added on. | 4.5 | Full comparison |
| Addepto | Mid-market to enterprise buyers in aviation, logistics, or... | Explicit 'proof-of-concept to production' positioning addresses the common failure mode where enterprise ML pilots never reach deployment. | 4.4 | Full comparison |
| InData Labs | Companies wanting a decade-plus data science track record... | Runs its own R&D center rather than purely project-based delivery, spanning generative AI/GPT integration through classic predictive analytics and computer vision. | 4.4 | Full comparison |
| Xomnia | Dutch and Northwest European enterprises wanting a single... | Acquired Aurai in 2025 specifically to consolidate strategy, platform, and applied-AI capability under one roof as it scales toward regional market leadership. | 4.3 | Full comparison |
| WeAreBrain | Startups and scale-ups wanting AI-native product development combined... | Frames itself around culture and retention — 'a winning team, not an agency' — with a long average client tenure as central to its pitch alongside technical delivery. | 4.3 | Full comparison |
| Deeper Insights | Enterprises across healthcare, real estate, and financial services... | Team holds 500+ citations and patents globally (per company website), signaling research depth rather than a purely delivery-focused staffing model. | 4.3 | Full comparison |
| Alexander Thamm | Large German and DACH-region enterprises — especially automotive... | 'Whitebox solutions' positioning emphasizes transparency and manufacturer independence, backed by 3,500+ completed projects and blue-chip automotive clients. | 4.2 | Full comparison |
| Nexocode | Startups and scale-ups wanting a small, senior AI... | Explicitly flat organizational structure with no traditional management hierarchy — every team member is described as equally involved in growth and delivery decisions. | 4.2 | Full comparison |
| Predli | Organizations wanting a structured path from first AI... | 'Predli Studio' is a dedicated build function that turns AI strategy directly into production-grade custom solutions, rather than handing delivery to a separate vendor. | 4.2 | Full comparison |
| Synergy Labs | French and EU businesses wanting practical, dashboard- and... | Focuses specifically on business-facing applied ML — smart dashboards, customer segmentation, recommendation engines — built to EU compliance rules, rather than broad AI R&D. | 4.1 | Full comparison |
| xtream | Italian and pan-European scale-ups wanting AI features embedded... | Combines UX design, product management, and software engineering with applied ML and BI — AI is delivered as part of a full digital-product build, not a bolt-on service. | 4.1 | Full comparison |
| element61 | Belgian and Benelux enterprises wanting a long-established analytics... | Started as an analytics and performance-management consultancy in 2007 and layered data science and AI on top of an already-mature BI practice, combining both under one roof. | 4.1 | Full comparison |
| Miquido | Companies wanting AI and ML features — RAG,... | Offers on-device AI development and AI guardrails alongside core ML, computer vision, and NLP work — a more product-engineering-centric AI offering than pure consulting-first competitors. | 4.1 | Full comparison |
| Neoteric | Companies wanting a well-reviewed, mid-size Polish AI and... | 4.9/5 rating across 70 verified Clutch reviews and 300+ completed projects across five continents gives an unusually large, independently verifiable review base for a company of this size. | 4.0 | Full comparison |
| Grape Up | Automotive and finance enterprises wanting agentic AI and... | Built its own productized platforms (Databoostr, Cloudboostr) alongside custom delivery — a hybrid product-plus-services model less common among pure consultancies on this list. | 4.0 | Full comparison |
| Deviniti | Enterprises in regulated or complex sectors wanting generative... | 50+ Atlassian-certified professionals and Atlassian Partner of the Year finalist status give it unusually strong enterprise-IT integration credibility alongside its generative AI practice and Bielik.AI open-source contributions. | 4.0 | Full comparison |
| STX Next | Enterprises wanting Python-native ML and AI engineering from... | Built and open-sourced DeepNext, an autonomous AI developer agent, and holds AWS Advanced Tier, Snowflake, Databricks, Azure, and Amazon Bedrock partnerships simultaneously. | 4.0 | Full comparison |
| CN Group CZ | Nordic, German, and Austrian enterprises wanting an established,... | Combines Scandinavian management style with Czech, Slovak, and Romanian engineering talent, and layers AI/ML onto a much older core business in embedded systems and industrial automation. | 3.9 | Full comparison |
| ASSIST Software | Manufacturing and agriculture clients in the DACH region... | Runs 25+ active R&D projects and participates in 25+ EU-funded research programs alongside 160+ research-institution partnerships — an unusually research-heavy profile for a 30+ year old nearshore vendor. | 3.9 | Full comparison |
| Software Mind | Large enterprises wanting AI/ML delivered alongside broader custom... | 48-month average client relationship length and ISO 9001/14001/27001 certification stack signal an enterprise-process-mature vendor built for long-term programs rather than short AI pilots. | 3.9 | Full comparison |
| Future Processing | Insurance, finance, and energy enterprises wanting an outcome-based... | Publicly states that 95% of generative AI pilots deliver no measurable return and positions its own outcome-based delivery approach against that failure pattern, backed by named case studies with hard percentage metrics. | 3.9 | Full comparison |
| SPD Technology | Fintech and payments companies wanting AI/ML delivered by... | Secured direct partnerships with OpenAI, Anthropic, and AWS specifically to reinforce its cloud and AI/ML capabilities — a more direct foundation-model-vendor relationship than most peers on this list disclose. | 3.9 | Full comparison |
| Zühlke | Large regulated enterprises — medtech, finance, industrial —... | Founded in 1968 as a product-innovation engineering firm, giving it a far longer institutional track record than any other company on this list — AI/ML is one current-generation capability within a much broader innovation-consulting practice. | 3.9 | Full comparison |
| Arnia Software | Companies needing deep R&D-level engineering — database engines,... | Machine learning expertise grew out of Arnia's original R&D work in database engines and operating systems, giving it lower-level systems engineering depth uncommon among application-focused AI vendors on this list. | 3.8 | Full comparison |
| Reaktor | Enterprises wanting AI capability embedded within a broader... | Co-created 'Elements of AI,' a free AI literacy MOOC with the University of Helsinki taken by over half a million people worldwide — a public-education contribution unmatched by any other company on this list. | 3.8 | Full comparison |
| Framna | Nordic and Benelux enterprises wanting mobile-first digital product... | Formed in 2023 through the merger of three established agencies backed by Waterland Private Equity, giving it unusually broad simultaneous coverage of Sweden, Denmark, the Netherlands, and Poland under one group. | 3.8 | Full comparison |
| N-iX | Large enterprises wanting AI-augmented software engineering at significant... | Legally headquartered in Valletta, Malta, with its primary engineering hub historically in Lviv, Ukraine; relocated 600+ Ukrainian engineers to safety in 2022 without dropping a single client project, and reports zero delivery disruptions since founding in 2002. | 3.8 | Full comparison |
| Sigma Software | Large enterprises wanting a Swedish-incorporated, EU-contractable IT consultancy... | 60% owned by the Swedish Sigma Group since 2006, giving Sigma Software a Swedish corporate parent and legal entity while its founding engineering culture and historical delivery base trace to Kharkiv, Ukraine. | 3.7 | Full comparison |
| Nordcloud (an IBM Company) | Large enterprises already committed to a major public... | Acquired by IBM in 2020 and now operates as an IBM subsidiary, giving it direct backing from one of the largest enterprise technology vendors globally, while holding all three major cloud certifications simultaneously. | 3.7 | Full comparison |
NILG.AI FAQ
What is NILG.AI?
NILG.AI is a Porto, Portugal AI consultancy founded in 2018 by Kelwin Fernandes (PhD, Computer Science, University of Porto) and Nohelia González. It runs a structured discover-pilot-scale methodology to help businesses identify high-impact AI opportunities, validate them, and scale what works, and has assisted over 100 companies across sectors. The company was incubated at UPTEC and was awarded Data Changemaker of the Year at DSPA Insights 2024 for an AI-driven urban waste-management project in the Algarve. Its YouTube education channel has over 100,000 subscribers and NILG.AI was selected for Microsoft's 'Learn with Creators' program.
How much does NILG.AI charge?
NILG.AI uses consulting engagement, pilot-to-scale retainer pricing. Minimum engagement starts at Not published. A discovery call is required to get project-specific quotes.
What tech stack does NILG.AI use?
NILG.AI works with Python, scikit-learn, Data pipelines, Cloud ML platforms. Primary industries served include Public Sector, Cross-industry AI adoption.
Is NILG.AI right for enterprise?
Companies earlier in their AI adoption curve that want a structured discover-pilot-scale engagement model rather than a from-scratch build.. 10–49 team size. Key consideration: 10–49 employee band limits capacity for running several large programs concurrently.
What are the best NILG.AI alternatives?
The best alternatives to NILG.AI depend on your use case. Top options are:
- dida Datenschmiede: team composed primarily of mathematicians and physicists, explicitly rejecting black-box tooling in favor of custom-built models as its sole service line.
- Tensorway: spun out of anadea's applied r&d unit in 2019, giving it a mature delivery bench uncommon for a five-year-old ai boutique.
- Neurons Lab: positions itself as an end-to-end ai enablement partner specifically for financial services, with governance and compliance tooling built into the core offer rather than added on.
Compare NILG.AI with other Machine Learning Development companies
NILG.AI vs dida Datenschmiede NILG.AI vs Tensorway NILG.AI vs Neurons Lab NILG.AI vs Addepto NILG.AI vs InData Labs NILG.AI vs Xomnia NILG.AI vs WeAreBrain NILG.AI vs Deeper Insights NILG.AI vs Alexander Thamm NILG.AI vs Nexocode NILG.AI vs Predli NILG.AI vs Synergy Labs NILG.AI vs xtream NILG.AI vs element61 NILG.AI vs Miquido NILG.AI vs Neoteric NILG.AI vs Grape Up NILG.AI vs Deviniti NILG.AI vs STX Next NILG.AI vs CN Group CZ NILG.AI vs ASSIST Software NILG.AI vs Software Mind NILG.AI vs Future Processing NILG.AI vs SPD Technology NILG.AI vs Zühlke NILG.AI vs Arnia Software NILG.AI vs Reaktor NILG.AI vs Framna NILG.AI vs N-iX NILG.AI vs Sigma Software NILG.AI vs Nordcloud (an IBM Company)
Last reviewed: July 2026. Verify all details directly with NILG.AI before making a decision.